Introduction
Smart IoT sensors, a.k.a. sensorless sensors, allow for real-time health monitoring of electronics without requiring external sensing devices. Performance sensing is the first step to implement PHM enabling to collect a history of time-dependent degradation of materials or environmental stresses. Sensors, in general, need to be attached internally or externally, which may not always be feasible and practical due to limited accessibility and interference in operation. Smart IoT sensors developing in ISR lab use digital signal characteristics to non-destructively assess the health of electronics in real-time.

Sensorless Sensing of Interconnect Health based on Digital Signal Characteristics
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–    Abstract
The health of electronic assemblies is largely affected by the degradation of interconnects, such as solder joints, component legs, and printed circuit board (PCB) traces. Interconnects are vulnerable to failures by a variety of mechanisms, including fatigue, mechanical over-stress, corrosion, and creep. Failure of these interconnects could cause the loss of connectivity between the components, which might eventually result in the loss of assembly functionality.
In order to detect interconnect failures, this study introduced an approach based on the skin effect of digital signal. Due to the skin effect, digital signal propagates through the exterior site of the conductor, and can be affected adversely by damages on the exterior site.

-    Reference
Lee, J., and Kwon, D. (2017) “Digital Signal-based Diagnosis of Interconnects Subjected to Degradation in Use Conditions,” Microelectronics Journal, vol. 60, pp. 87-93.
Yoon, J., and Kwon, D. (2016) “A Model-based Prognostic Approach to Predict Remaining Useful Life of Interconnects using Impedance Analysis,” Journal of Mechanical Science and Technology, vol. 30(10), pp. 4447-4452.

Remaining-Life Prediction of Solder Joints Using RF Impedance Analysis and Gaussian Process Regression
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–    Abstract
Solder joints are among the most common failure sites in electronic assemblies. This paper presents a prognostic approach that allows for remaining useful life prediction of solder joints using RF impedance analysis and Gaussian process (GP) regression. While solder joints were exposed to a mechanical stress condition to generate fatigue failures, the RF impedance of the solder joint was continuously monitored. The RF impedance provided early indication of impending solder joint failure in the form of a gradual increase prior to the end of life. A GP model was applied to the RF impedance obtained from the fatigue tests in order to estimate solder joint remaining life in real time. It was demonstrated that the GP model successfully predicted the time to solder joint failure with high accuracy prior to failure. The prediction performance was also evaluated using prognostic metrics.

-    Reference
Kwon, D., Azarian, M. H., and Pecht, M. (2015) “Remaining Life Prediction of Solder Joints Using RF Impedance Analysis and Gaussian Process Regression,” IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 5(11), pp. 1602-1609.